{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path = os.path.join(housing_path,'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() #查看数据集的简单描述 得到每个属性的类型和非空值的数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total_bedrooms 20433 有一部分空值\n",
    "housing['ocean_proximity'].value_counts()#查看多少种分类 5种分类，获得每种分类下的区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() #显示数值属性摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50,figsize = (20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房龄和房价被设定了上限 \n",
    "  1. 对设置了上限的区域重新收集标签值\n",
    "  2. 把50万美元的区域从数据集中移除\n",
    "  \n",
    "### 重尾现象 头高尾长\n",
    "   * 转换数据，把数据变成偏钟形分布（正态分布，平均值为0，标准差为1）\n",
    "   \n",
    "### 收入特征\n",
    "   * 年薪 单位为万美元，提前对特征进行缩放是正常的，需得知数据是如何缩放的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "   * 训练集用于模型训练 占整个数据集的80%，测试集占20%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data,test_ratio):\n",
    "    np.random.seed(42)\n",
    "    indices = np.random.permutation(len(data)) # 对原来的数据进行重新洗牌，随机打乱原来的元素顺序\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices],data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test(housing,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对样本设置唯一的标识符，对标识符取得哈希值，取哈希值的最后一个字节，这个值<=51，256*20%，放入测试集\n",
    "# 哈希值相同的两个对象不一定相同，哈希值不同的两个对象一定不是同一个对象\n",
    "import hashlib\n",
    "def test_set_check(identification,test_radio,hash = hashlib.md5):\n",
    "    return hash(np.int64(identification)).digest()[-1] < 256 * test_ratio #返回摘要，把hash对象转化为二进制字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 另一种拆分训练集和测试集的方法\n",
    "def split_train_test_by_id(data,test_ratio,id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_,test_ratio))\n",
    "    return data.loc[-in_test_set],data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-693b2b9e8bc2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhousing_with_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhousing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#使用行索引作为标识符 ID\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'housing' is not defined"
     ]
    }
   ],
   "source": [
    "housing_with_id = housing.reset_index() #使用行索引作为标识符 ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
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       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
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       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(housing_with_id,0.2,'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
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       "      <td>6</td>\n",
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       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基于行索引，不能删除和从中间插入数据，只能从末尾插入，否则行索引汇编\n",
    "# 寻找一个稳定的特征作为唯一标识"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1138.0</td>\n",
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       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(housing_with_id,0.2,'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set,test_set = train_test_split(housing,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>14196</td>\n",
       "      <td>-117.03</td>\n",
       "      <td>32.71</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>3.2596</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8267</td>\n",
       "      <td>-118.16</td>\n",
       "      <td>33.77</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3382.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>382100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17445</td>\n",
       "      <td>-120.48</td>\n",
       "      <td>34.66</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>172600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14265</td>\n",
       "      <td>-117.11</td>\n",
       "      <td>32.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>93400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2271</td>\n",
       "      <td>-119.80</td>\n",
       "      <td>36.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从数据可视化中探寻数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分层采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 希望测试集能够代表整个数据集中各种不同类型的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 创建一个收入类别属性\n",
    "   2. 不应该将数据分层太多，但每一层都应该有足够的数据量\n",
    "   3. 将收入中位数除以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的类别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].head(20)\n",
    "\n",
    "housing['income_cat'].where(housing['income_cat']<5,5.0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = pd.cut(housing['median_income'],bins = [0.,1.5,3.0,4.5,6.,np.inf],labels = [1,2,3,4,5]) #把连续值转换成类别标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     5\n",
       "2     5\n",
       "3     4\n",
       "4     3\n",
       "5     3\n",
       "6     3\n",
       "7     3\n",
       "8     2\n",
       "9     3\n",
       "10    3\n",
       "11    3\n",
       "12    3\n",
       "13    2\n",
       "14    2\n",
       "15    2\n",
       "16    2\n",
       "17    2\n",
       "18    2\n",
       "19    2\n",
       "Name: income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制收入类别直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bc61dd1ac8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据收入类别进行分层采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits = 1,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index,test_index in split.split(housing,housing['income_cat']):\n",
    "    start_train_set = housing.loc[train_index]\n",
    "    start_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19480</td>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8879</td>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13685</td>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>140200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4937</td>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4861</td>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16365</td>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>92100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19684</td>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19234</td>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13956</td>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2390</td>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>123900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11176</td>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>197400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15614</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2953</td>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>63300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13209</td>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6569</td>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>184200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "19480      1448.0       570.0         3.5395            127900.0   \n",
       "8879        769.0       277.0         8.3839            500001.0   \n",
       "13685       936.0       375.0         6.0000            140200.0   \n",
       "4937       1916.0       438.0         1.8242             95000.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "16365      2734.0       879.0         2.7981             92100.0   \n",
       "19684      1202.0       506.0         1.6902             61500.0   \n",
       "19234      1442.0       506.0         6.6854            313000.0   \n",
       "13956       416.0       151.0         2.7120             89000.0   \n",
       "2390       1184.0       429.0         3.9141            123900.0   \n",
       "11176      1758.0       593.0         3.3831            197400.0   \n",
       "15614       592.0       441.0         3.6728            500001.0   \n",
       "2953        776.0       272.0         1.9830             63300.0   \n",
       "13209      1243.0       394.0         4.0614            107000.0   \n",
       "6569        857.0       269.0         4.5000            184200.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  \n",
       "19480          INLAND          3  \n",
       "8879        <1H OCEAN          5  \n",
       "13685          INLAND          4  \n",
       "4937        <1H OCEAN          2  \n",
       "4861        <1H OCEAN          1  \n",
       "16365          INLAND          2  \n",
       "19684          INLAND          2  \n",
       "19234       <1H OCEAN          5  \n",
       "13956          INLAND          2  \n",
       "2390           INLAND          3  \n",
       "11176       <1H OCEAN          3  \n",
       "15614        NEAR BAY          3  \n",
       "2953           INLAND          2  \n",
       "13209          INLAND          3  \n",
       "6569           INLAND          3  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比\n",
    "start_test_set['income_cat'].value_counts()/len(start_test_set) # 分层抽样测试集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing) #完整数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试集已经处理完成，用分层抽样的训练集数据进行分析\n",
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  将地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立一个各区域的分布图，以便于数据可视化\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bc6172e348>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-e91c832a5982>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 将alpha值（不透明度，0-1）设置为0.1，可以看出高密度数据点的位置\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mhousing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'scatter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'longitude'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'latitude'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'housing' is not defined"
     ]
    }
   ],
   "source": [
    "# 将alpha值（不透明度，0-1）设置为0.1，可以看出高密度数据点的位置\n",
    "housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude',alpha = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bc616cba88>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude',alpha = 0.4,\n",
    "             s = housing['population']/100,label = 'population',figsize=(10,7),\n",
    "             c = 'median_house_value',cmap = plt.get_cmap('jet'),colorbar = True,\n",
    "             sharex = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "california_img = mpimg.imread('./california.png')\n",
    "\n",
    "housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude',alpha = 0.4,\n",
    "             s = housing['population']/100,label = 'population',figsize=(10,7),\n",
    "             c = 'median_house_value',cmap = plt.get_cmap('jet'),colorbar = False,\n",
    "             sharex = False)\n",
    "\n",
    "plt.imshow(california_img,extent = [-124.55,-113.80,32.45,42.05],alpha = 0.5,\n",
    "          cmap = plt.get_cmap('jet'))\n",
    "plt.ylabel('Latitude',fontsize = 14)\n",
    "plt.xlabel('Longitude',fontsize = 14)\n",
    "\n",
    "prices = housing['median_house_value']\n",
    "tick_values = np.linspace(prices.min(),prices.max(),11)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels(['$%dk'%(round(v/1000)) for v in tick_values],fontsize = 14)\n",
    "cbar.set_label('Median House Value',fontsize = 16)\n",
    "\n",
    "plt.legend(fontsize = 16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 房屋价格与地理位置靠海和人口密度息息相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>longitude</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>latitude</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_rooms</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_bedrooms</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>population</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>households</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>median_income</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>median_house_value</td>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr() 方法：可以就计算出每对特征之间的相关系数，称为皮尔逊相关系数\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 相关系数范围从 -1 变化到 1，越接近 1 ，表示越强的正相关， 比如当收入中位数上升时，房价中位数也趋于上升。\n",
    "   * 当系数接近 -1 ，则表示有强烈的负相关， 纬度和房价中位数之间出现轻微的负相关性，越往北走，房价越倾向于下降\n",
    "   * 系数接近0，说明二者之间没有线性相关性\n",
    "   * 相关系数仅检测线性相关性，如果X上升，y上升或下降，可能会彻底遗漏非线性相关性，例如 如果x接近于0，y上升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用pandas，scatter_matrix 函数可以绘制特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64784888>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC648DBB08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC6490CD48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64AC7388>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64AFA9C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64B305C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64B66FC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64B95EC8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64B9CD48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64BD2DC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64DA1388>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64DDC448>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64E15548>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64E52E88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64E86788>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000002BC64EBF888>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attributes = ['median_house_value','median_income','total_rooms','housing_median_age']\n",
    "scatter_matrix(housing[attributes],figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 16, 0, 550000]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter',x = 'median_income',y = 'median_house_value',alpha = 0.1)\n",
    "plt.axis([0,16,0,550000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 在给机器学习算法输入数据之前，我们识别出了一些异常数据，需要提前清理掉，也发现了不同属性之间的某些联系，跟目标属性相关的联系\n",
    "   2. 某些属性分布显示出了明显的“重尾”分布，对此进行转换处理，计算其对数\n",
    "   3. 在给机器学习算法输入数据之前，最后一件事，尝试各种属性的组合\n",
    "   4. 每个项目历程不一样，大致思路都相似"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试验不同特征的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedsroom_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedsroom_per_room</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>0.146591</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.155797</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>0.129516</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>0.184458</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>0.172096</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20635</td>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.045455</td>\n",
       "      <td>0.224625</td>\n",
       "      <td>2.560606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20636</td>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>0.215208</td>\n",
       "      <td>3.122807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20637</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>0.215173</td>\n",
       "      <td>2.325635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20638</td>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>0.219892</td>\n",
       "      <td>2.123209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20639</td>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>0.221185</td>\n",
       "      <td>2.616981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "0           322.0       126.0         8.3252            452600.0   \n",
       "1          2401.0      1138.0         8.3014            358500.0   \n",
       "2           496.0       177.0         7.2574            352100.0   \n",
       "3           558.0       219.0         5.6431            341300.0   \n",
       "4           565.0       259.0         3.8462            342200.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "20635       845.0       330.0         1.5603             78100.0   \n",
       "20636       356.0       114.0         2.5568             77100.0   \n",
       "20637      1007.0       433.0         1.7000             92300.0   \n",
       "20638       741.0       349.0         1.8672             84700.0   \n",
       "20639      1387.0       530.0         2.3886             89400.0   \n",
       "\n",
       "      ocean_proximity income_cat  rooms_per_household  bedsroom_per_room  \\\n",
       "0            NEAR BAY          5             6.984127           0.146591   \n",
       "1            NEAR BAY          5             6.238137           0.155797   \n",
       "2            NEAR BAY          5             8.288136           0.129516   \n",
       "3            NEAR BAY          4             5.817352           0.184458   \n",
       "4            NEAR BAY          3             6.281853           0.172096   \n",
       "...               ...        ...                  ...                ...   \n",
       "20635          INLAND          2             5.045455           0.224625   \n",
       "20636          INLAND          2             6.114035           0.215208   \n",
       "20637          INLAND          2             5.205543           0.215173   \n",
       "20638          INLAND          2             5.329513           0.219892   \n",
       "20639          INLAND          2             5.254717           0.221185   \n",
       "\n",
       "       population_per_household  \n",
       "0                      2.555556  \n",
       "1                      2.109842  \n",
       "2                      2.802260  \n",
       "3                      2.547945  \n",
       "4                      2.181467  \n",
       "...                         ...  \n",
       "20635                  2.560606  \n",
       "20636                  3.122807  \n",
       "20637                  2.325635  \n",
       "20638                  2.123209  \n",
       "20639                  2.616981  \n",
       "\n",
       "[20640 rows x 14 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "rooms_per_household         0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedsroom_per_room          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关联矩阵\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习算法的数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到一个分层抽样代表全局数据集的 训练集和测试集\n",
    "start_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据集的数据和标签分开\n",
    "housing_labels = start_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = start_train_set.drop('median_house_value',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
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       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
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       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN          2  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN          5  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN          2  \n",
       "3230       1460.0       353.0         1.8839          INLAND          2  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN          3  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 10 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "ocean_proximity       16512 non-null object\n",
      "income_cat            16512 non-null category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 大多数机器学习算法无法在缺失的特征上工作，创建一些函数辅助，total_bedsroom有缺失\n",
    "   * 放弃这些区域\n",
    "   * 放弃这个属性\n",
    "   * 将缺失值设置为某个值，0，平均数或者中位数都行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4629</td>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6068</td>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17923</td>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13656</td>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19252</td>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN          2  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN          4  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN          4  \n",
       "13656      1039.0       391.0         1.6675          INLAND          2  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN          3  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis = 1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\think\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 放弃为空值的区域\n",
    "#rows.dropna(subset = ['total_bedrooms'])\n",
    "# 放弃这个属性\n",
    "#rows.drop('total_bedrooms',axis = 1)\n",
    "\n",
    "#将缺失值设置为某个值，0，平均数或中位数等\n",
    "#创建一个imputer实例，指定你要用属性中的中位数值替换该属性的缺失值\n",
    "from sklearn.preprocessing import Imputer as SimpleImputer\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "#使用fit()方法将imputer实例适配到训练集\n",
    "housing_num = housing.drop('ocean_proximity',axis = 1)\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89  ,   37.29  ,   38.    , ...,  339.    ,    2.7042,\n",
       "           2.    ],\n",
       "       [-121.93  ,   37.05  ,   14.    , ...,  113.    ,    6.4214,\n",
       "           5.    ],\n",
       "       [-117.2   ,   32.77  ,   31.    , ...,  462.    ,    2.8621,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-116.4   ,   34.09  ,    9.    , ...,  765.    ,    3.2723,\n",
       "           3.    ],\n",
       "       [-118.01  ,   33.82  ,   31.    , ...,  356.    ,    4.0625,\n",
       "           3.    ],\n",
       "       [-122.45  ,   37.77  ,   52.    , ...,  639.    ,    3.575 ,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X,columns = housing_num.columns,index = housing.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14650</td>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "17606       710.0       339.0         2.7042         2.0  \n",
       "18632       306.0       113.0         6.4214         5.0  \n",
       "14650       936.0       462.0         2.8621         2.0  \n",
       "3230       1460.0       353.0         1.8839         2.0  \n",
       "3555       4459.0      1463.0         3.0347         3.0  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16512 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "income_cat            16512 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-Learn\n",
    "   * 一致性\n",
    "    1. 估算器\n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7276\n",
       "INLAND        5263\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)\n",
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将文本转化成对应的数字分类，使用转换器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoder = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 机器学习算法会认为两个相近的数字比远的数字更相似，比如 0，1 和 0，4 相比，0，1相似度更高\n",
    "   2. 创建独热编码，当 <1H 时，第0个属性为1，其余为0，类别是inland时 另一个属性为1，其余为0\n",
    "   3. OneHotEncoder编码器，fit_trans需要二维数组，转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OneHotEncoder编码器，fit_trans需要二维数组，转换housing_cat\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [4],\n",
       "       ...,\n",
       "       [1],\n",
       "       [0],\n",
       "       [3]])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\think\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_onehot = encoder.fit_transform(housing_cat_encoder.reshape(-1,1))\n",
    "housing_cat_onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从稀疏矩阵转化成numpy\n",
    "housing_cat_onehot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606     <1H OCEAN\n",
       "18632     <1H OCEAN\n",
       "14650    NEAR OCEAN\n",
       "3230         INLAND\n",
       "3555      <1H OCEAN\n",
       "            ...    \n",
       "6563         INLAND\n",
       "12053        INLAND\n",
       "13908        INLAND\n",
       "11159     <1H OCEAN\n",
       "15775      NEAR BAY\n",
       "Name: ocean_proximity, Length: 16512, dtype: object"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一次完成两个转化  文本——整数类型——独热类型\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelBinarizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_cat_onehot = encoder.fit_transform(housing_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出稀疏矩阵\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output = True)\n",
    "housing_cat_onehot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_onehot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "\n",
    "rooms_ix,bedrooms_ix,population_ix,household_ix = [list(housing.columns).index(col) for col in ('total_rooms','total_bedrooms','population','households')]\n",
    "\n",
    "class CombinedAttributeAdder(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    \n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self,X,y = None):\n",
    "        rooms_per_household = X[:,rooms_ix] / X[:,household_ix]\n",
    "        population_per_household = X[:,population_ix] / X[:,household_ix]\n",
    "        bedrooms_per_room = X[:,bedrooms_ix] / X[:,rooms_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:,bedrooms_ix] / X[:,rooms_ix]\n",
    "            return np.c_[X,rooms_per_household,population_per_household,bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X,rooms_per_household,population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributeAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., 2, 4.625368731563422,\n",
       "        2.094395280235988],\n",
       "       [-121.93, 37.05, 14.0, ..., 5, 6.008849557522124,\n",
       "        2.7079646017699117],\n",
       "       [-117.2, 32.77, 31.0, ..., 2, 4.225108225108225,\n",
       "        2.0259740259740258],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 3, 6.34640522875817, 2.742483660130719],\n",
       "       [-118.01, 33.82, 31.0, ..., 3, 5.50561797752809,\n",
       "        3.808988764044944],\n",
       "       [-122.45, 37.77, 52.0, ..., 3, 4.843505477308295,\n",
       "        1.9859154929577465]], dtype=object)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1   2     3     4     5     6       7           8  9       10  \\\n",
       "0 -121.89  37.29  38  1568   351   710   339  2.7042   <1H OCEAN  2  4.62537   \n",
       "1 -121.93  37.05  14   679   108   306   113  6.4214   <1H OCEAN  5  6.00885   \n",
       "2  -117.2  32.77  31  1952   471   936   462  2.8621  NEAR OCEAN  2  4.22511   \n",
       "3 -119.61  36.31  25  1847   371  1460   353  1.8839      INLAND  2  5.23229   \n",
       "4 -118.59  34.23  17  6592  1525  4459  1463  3.0347   <1H OCEAN  3  4.50581   \n",
       "\n",
       "        11  \n",
       "0   2.0944  \n",
       "1  2.70796  \n",
       "2  2.02597  \n",
       "3  4.13598  \n",
       "4  3.04785  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17606</td>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
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       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18632</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
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       "      <td>14650</td>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3230</td>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3555</td>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "17606        710        339        2.7042       <1H OCEAN          2   \n",
       "18632        306        113        6.4214       <1H OCEAN          5   \n",
       "14650        936        462        2.8621      NEAR OCEAN          2   \n",
       "3230        1460        353        1.8839          INLAND          2   \n",
       "3555        4459       1463        3.0347       <1H OCEAN          3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "17606             4.62537                   2.0944  \n",
       "18632             6.00885                  2.70796  \n",
       "14650             4.22511                  2.02597  \n",
       "3230              5.23229                  4.13598  \n",
       "3555              4.50581                  3.04785  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns = list(housing.columns)+['rooms_per_household','population_per_household'],\n",
    "    index = housing.index)\n",
    "\n",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5019</td>\n",
       "      <td>-118.32</td>\n",
       "      <td>33.99</td>\n",
       "      <td>43</td>\n",
       "      <td>2028</td>\n",
       "      <td>479</td>\n",
       "      <td>1074</td>\n",
       "      <td>394</td>\n",
       "      <td>2.5909</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.14721</td>\n",
       "      <td>2.72589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8187</td>\n",
       "      <td>-118.11</td>\n",
       "      <td>33.78</td>\n",
       "      <td>16</td>\n",
       "      <td>3985</td>\n",
       "      <td>567</td>\n",
       "      <td>1327</td>\n",
       "      <td>564</td>\n",
       "      <td>7.9767</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0656</td>\n",
       "      <td>2.35284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16730</td>\n",
       "      <td>-119.93</td>\n",
       "      <td>35.2</td>\n",
       "      <td>29</td>\n",
       "      <td>1649</td>\n",
       "      <td>342</td>\n",
       "      <td>671</td>\n",
       "      <td>264</td>\n",
       "      <td>3.0602</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>6.24621</td>\n",
       "      <td>2.54167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9168</td>\n",
       "      <td>-118.56</td>\n",
       "      <td>34.42</td>\n",
       "      <td>2</td>\n",
       "      <td>966</td>\n",
       "      <td>270</td>\n",
       "      <td>233</td>\n",
       "      <td>169</td>\n",
       "      <td>1.9667</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.71598</td>\n",
       "      <td>1.3787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12494</td>\n",
       "      <td>-121.47</td>\n",
       "      <td>38.57</td>\n",
       "      <td>50</td>\n",
       "      <td>3233</td>\n",
       "      <td>968</td>\n",
       "      <td>1223</td>\n",
       "      <td>837</td>\n",
       "      <td>1.2041</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8626</td>\n",
       "      <td>1.46117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16971</td>\n",
       "      <td>-122.31</td>\n",
       "      <td>37.55</td>\n",
       "      <td>45</td>\n",
       "      <td>507</td>\n",
       "      <td>140</td>\n",
       "      <td>305</td>\n",
       "      <td>139</td>\n",
       "      <td>2.6159</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.64748</td>\n",
       "      <td>2.19424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3112</td>\n",
       "      <td>-117.64</td>\n",
       "      <td>35.61</td>\n",
       "      <td>10</td>\n",
       "      <td>2656</td>\n",
       "      <td>506</td>\n",
       "      <td>1349</td>\n",
       "      <td>501</td>\n",
       "      <td>4.25</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.3014</td>\n",
       "      <td>2.69261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2668</td>\n",
       "      <td>-115.46</td>\n",
       "      <td>33.19</td>\n",
       "      <td>33</td>\n",
       "      <td>1234</td>\n",
       "      <td>373</td>\n",
       "      <td>777</td>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1</td>\n",
       "      <td>4.14094</td>\n",
       "      <td>2.60738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13839</td>\n",
       "      <td>-117.3</td>\n",
       "      <td>34.54</td>\n",
       "      <td>31</td>\n",
       "      <td>1174</td>\n",
       "      <td>360</td>\n",
       "      <td>1161</td>\n",
       "      <td>328</td>\n",
       "      <td>1.06</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1</td>\n",
       "      <td>3.57927</td>\n",
       "      <td>3.53963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16004</td>\n",
       "      <td>-122.46</td>\n",
       "      <td>37.75</td>\n",
       "      <td>52</td>\n",
       "      <td>1849</td>\n",
       "      <td>287</td>\n",
       "      <td>695</td>\n",
       "      <td>258</td>\n",
       "      <td>6.5372</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "      <td>7.16667</td>\n",
       "      <td>2.6938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16858</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.63</td>\n",
       "      <td>39</td>\n",
       "      <td>4220</td>\n",
       "      <td>1055</td>\n",
       "      <td>2720</td>\n",
       "      <td>1046</td>\n",
       "      <td>2.639</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.03442</td>\n",
       "      <td>2.60038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7372</td>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.97</td>\n",
       "      <td>44</td>\n",
       "      <td>2748</td>\n",
       "      <td>715</td>\n",
       "      <td>2962</td>\n",
       "      <td>703</td>\n",
       "      <td>2.6951</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.90896</td>\n",
       "      <td>4.21337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14638</td>\n",
       "      <td>-117.19</td>\n",
       "      <td>32.79</td>\n",
       "      <td>36</td>\n",
       "      <td>1514</td>\n",
       "      <td>258</td>\n",
       "      <td>665</td>\n",
       "      <td>278</td>\n",
       "      <td>3.8571</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.44604</td>\n",
       "      <td>2.39209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15824</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>37.75</td>\n",
       "      <td>52</td>\n",
       "      <td>1974</td>\n",
       "      <td>525</td>\n",
       "      <td>935</td>\n",
       "      <td>465</td>\n",
       "      <td>2.7173</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "      <td>4.24516</td>\n",
       "      <td>2.01075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6712</td>\n",
       "      <td>-118.17</td>\n",
       "      <td>34.14</td>\n",
       "      <td>45</td>\n",
       "      <td>2257</td>\n",
       "      <td>285</td>\n",
       "      <td>759</td>\n",
       "      <td>305</td>\n",
       "      <td>11.7894</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.48852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11032</td>\n",
       "      <td>-117.84</td>\n",
       "      <td>33.78</td>\n",
       "      <td>24</td>\n",
       "      <td>3817</td>\n",
       "      <td>787</td>\n",
       "      <td>1656</td>\n",
       "      <td>713</td>\n",
       "      <td>4.25</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.35344</td>\n",
       "      <td>2.32258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11769</td>\n",
       "      <td>-121.36</td>\n",
       "      <td>38.73</td>\n",
       "      <td>21</td>\n",
       "      <td>2253</td>\n",
       "      <td>416</td>\n",
       "      <td>1050</td>\n",
       "      <td>411</td>\n",
       "      <td>3.141</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.48175</td>\n",
       "      <td>2.55474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10915</td>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.73</td>\n",
       "      <td>45</td>\n",
       "      <td>2264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970</td>\n",
       "      <td>499</td>\n",
       "      <td>3.4193</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.53707</td>\n",
       "      <td>3.9479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9410</td>\n",
       "      <td>-122.56</td>\n",
       "      <td>37.9</td>\n",
       "      <td>24</td>\n",
       "      <td>221</td>\n",
       "      <td>41</td>\n",
       "      <td>75</td>\n",
       "      <td>38</td>\n",
       "      <td>5.1292</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "      <td>5.81579</td>\n",
       "      <td>1.97368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3657</td>\n",
       "      <td>-118.4</td>\n",
       "      <td>34.21</td>\n",
       "      <td>30</td>\n",
       "      <td>2453</td>\n",
       "      <td>544</td>\n",
       "      <td>1753</td>\n",
       "      <td>506</td>\n",
       "      <td>2.9803</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.84783</td>\n",
       "      <td>3.46443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "5019    -118.32    33.99                 43        2028            479   \n",
       "8187    -118.11    33.78                 16        3985            567   \n",
       "16730   -119.93     35.2                 29        1649            342   \n",
       "9168    -118.56    34.42                  2         966            270   \n",
       "12494   -121.47    38.57                 50        3233            968   \n",
       "16971   -122.31    37.55                 45         507            140   \n",
       "3112    -117.64    35.61                 10        2656            506   \n",
       "2668    -115.46    33.19                 33        1234            373   \n",
       "13839    -117.3    34.54                 31        1174            360   \n",
       "16004   -122.46    37.75                 52        1849            287   \n",
       "16858   -122.41    37.63                 39        4220           1055   \n",
       "7372    -118.23    33.97                 44        2748            715   \n",
       "14638   -117.19    32.79                 36        1514            258   \n",
       "15824   -122.42    37.75                 52        1974            525   \n",
       "6712    -118.17    34.14                 45        2257            285   \n",
       "11032   -117.84    33.78                 24        3817            787   \n",
       "11769   -121.36    38.73                 21        2253            416   \n",
       "10915   -117.87    33.73                 45        2264            NaN   \n",
       "9410    -122.56     37.9                 24         221             41   \n",
       "3657     -118.4    34.21                 30        2453            544   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "5019        1074        394        2.5909       <1H OCEAN          2   \n",
       "8187        1327        564        7.9767       <1H OCEAN          5   \n",
       "16730        671        264        3.0602          INLAND          3   \n",
       "9168         233        169        1.9667       <1H OCEAN          2   \n",
       "12494       1223        837        1.2041          INLAND          1   \n",
       "16971        305        139        2.6159      NEAR OCEAN          2   \n",
       "3112        1349        501          4.25          INLAND          3   \n",
       "2668         777        298             1          INLAND          1   \n",
       "13839       1161        328          1.06          INLAND          1   \n",
       "16004        695        258        6.5372        NEAR BAY          5   \n",
       "16858       2720       1046         2.639      NEAR OCEAN          2   \n",
       "7372        2962        703        2.6951       <1H OCEAN          2   \n",
       "14638        665        278        3.8571      NEAR OCEAN          3   \n",
       "15824        935        465        2.7173        NEAR BAY          2   \n",
       "6712         759        305       11.7894       <1H OCEAN          5   \n",
       "11032       1656        713          4.25       <1H OCEAN          3   \n",
       "11769       1050        411         3.141          INLAND          3   \n",
       "10915       1970        499        3.4193       <1H OCEAN          3   \n",
       "9410          75         38        5.1292        NEAR BAY          4   \n",
       "3657        1753        506        2.9803       <1H OCEAN          2   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "5019              5.14721                  2.72589  \n",
       "8187               7.0656                  2.35284  \n",
       "16730             6.24621                  2.54167  \n",
       "9168              5.71598                   1.3787  \n",
       "12494              3.8626                  1.46117  \n",
       "16971             3.64748                  2.19424  \n",
       "3112               5.3014                  2.69261  \n",
       "2668              4.14094                  2.60738  \n",
       "13839             3.57927                  3.53963  \n",
       "16004             7.16667                   2.6938  \n",
       "16858             4.03442                  2.60038  \n",
       "7372              3.90896                  4.21337  \n",
       "14638             5.44604                  2.39209  \n",
       "15824             4.24516                  2.01075  \n",
       "6712                  7.4                  2.48852  \n",
       "11032             5.35344                  2.32258  \n",
       "11769             5.48175                  2.55474  \n",
       "10915             4.53707                   3.9479  \n",
       "9410              5.81579                  1.97368  \n",
       "3657              4.84783                  3.46443  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征缩放\n",
    "   * 最重要也是最需要应用在数据上的转换器就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "   * 房间总数范围6-39320，收入中位数范围0-15\n",
    "   * 目标值通常不需要缩放\n",
    "   * 同比缩放所有属性有两种方法，最小-最大缩放和标准化\n",
    "   * 最小-最大缩放又叫归一化，将值重新缩放到0-1之间，将值减去最小值并除以最大值和最小值差，如果不希望是0-1，可以调整超参数feature_range\n",
    "   * 标准化 减去平均值，所以标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "   * 缩放器仅用来拟合训练集，不是完成的数据集\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转换流水线\n",
    "   * 许多数据转化的步骤需要以正确的顺序来进行，PipeLine来支持这样的转换\n",
    "   * pipline构造函数会通过一系列名称、估算器的配对来定义步骤的序列，必须是转换器，必须要有fit_transform()方法\n",
    "   * 调用流水线的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最后\n",
    "   * 估算器，只会调用fit方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\think\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer',SimpleImputer(strategy = 'median')),\n",
    "    ('attribs_adder',CombinedAttributeAdder()),\n",
    "    ('std_scaler',StandardScaler())\n",
    "])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "income_cat            16512 non-null category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame--series--ndarray\n",
    "class DataFrameSelector(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,attributes_names):\n",
    "        self.attributes_names = attributes_names\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X):\n",
    "        return X[self.attributes_names].values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs = list(housing_num)\n",
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\think\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(num_attribs)),\n",
    "    ('Imputer',SimpleImputer(strategy='median')),\n",
    "    ('attribs_adder',CombinedAttributeAdder()),\n",
    "    ('std_scaler',StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self,*args,**kwargs):\n",
    "        self.encoder = LabelBinarizer(*args,**kwargs)\n",
    "    def fit(self,x,y =0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self,x,y = 0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(cat_attribs)),\n",
    "    ('LabelBinarizer',MyLabelBinarizer()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    ('num_pipeline',num_pipeline),\n",
    "    ('cat_pipeline',cat_pipeline)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_finished = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_finished"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
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       "      <td>-0.954456</td>\n",
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       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "          7         8         9        10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
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       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_finally = pd.DataFrame(housing_finished)\n",
    "housing_finally.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型\n",
    "   * 获得了数据\n",
    "   * 数据探索\n",
    "   * 对训练集和测试集进行拆分\n",
    "   * 编写了转换数据流水线\n",
    "   * 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
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       "      <td>3</td>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
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       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "          7         8         9        10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
       "1  1.336459  1.890305  0.217683 -0.033534 -0.836289  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(housing_finally)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 生成估算器对象，线性回归算法\n",
    "LR = LinearRegression()\n",
    "# 将数据匹配到算法中\n",
    "LR.fit(housing_finally,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [203682.37379543 326371.39370781 204218.64588245  58685.4770482\n",
      " 194213.06443039]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[0:5]\n",
    "\n",
    "#流水线转换数据 \n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\",LR.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606    286600.0\n",
       "18632    340600.0\n",
       "14650    196900.0\n",
       "3230      46300.0\n",
       "3555     254500.0\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376.64295459937"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算均方根误差\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = LR.predict(housing_finally)\n",
    "lin_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n 大多数地区的房屋中位数在120000到265000美元之间，预测误差高达68376，这是一个典型的模型对训练数据拟合不足的案例\\n 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大，\\n 1. 选择强大的模型\\n 2. 为算法提供更好的特征\\n 3. 减少对模型的限制等方法\\n'"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测值与真实值的平均误差为68376多\n",
    "'''\n",
    " 大多数地区的房屋中位数在120000到265000美元之间，预测误差高达68376，这是一个典型的模型对训练数据拟合不足的案例\n",
    " 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大，\n",
    " 1. 选择强大的模型\n",
    " 2. 为算法提供更好的特征\n",
    " 3. 减少对模型的限制等方法\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 决策树可以找到复杂的非线性关系\n",
    "\n",
    "\n",
    "#引进决策树估算器\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_reg = DecisionTreeRegressor(random_state = 42)\n",
    "tree_reg.fit(housing_finally,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 验证模型\n",
    "housing_predictions = tree_reg.predict(housing_finally)\n",
    "tree_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "#过于完美，也可能是这个模型对数据严重过度拟合了，如何确认？不要轻易启动测试集，拿训练集中的一部分用于训练，另一部分用于模型的验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70274.7991723 , 67258.3624668 , 71350.42593227, 68882.91340979,\n",
       "       70987.99296566, 74177.52037059, 70788.57311306, 70850.53018019,\n",
       "       76430.62239321, 70212.6471067 ])"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用train_test_split函数将训练集分为较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "# sklearn的交叉验证，将训练集随机分割成10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个折叠进行评估，\n",
    "# 另外9个进行评估\n",
    "\n",
    "#决策树交叉验证\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# neg_mean_squared_error是均方差回归损失，该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n",
    "scores = cross_val_score(tree_reg,housing_finally,housing_labels,\n",
    "                       scoring = 'neg_mean_squared_error',cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70274.7991723  67258.3624668  71350.42593227 68882.91340979\n",
      " 70987.99296566 74177.52037059 70788.57311306 70850.53018019\n",
      " 76430.62239321 70212.6471067 ]\n",
      "Mean 71121.4387110585\n",
      "Standard deviation 2434.3080046605132\n"
     ]
    }
   ],
   "source": [
    "# 输出10次的平均值和标准差\n",
    "def display_scores(scores):\n",
    "    print('Scores:',scores)\n",
    "    print('Mean',scores.mean())\n",
    "    print('Standard deviation',scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66877.52325028 66608.120256   70575.91118868 74179.94799352\n",
      " 67683.32205678 71103.16843468 64782.65896552 67711.29940352\n",
      " 71080.40484136 67687.6384546 ]\n",
      "Mean 68828.99948449328\n",
      "Standard deviation 2662.761570610345\n"
     ]
    }
   ],
   "source": [
    "# 线性交叉验证\n",
    "lin_scores = cross_val_score(LR,housing_finally,housing_labels,\n",
    "                            scoring = \"neg_mean_squared_error\",cv = 10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators = 10,random_state = 42)\n",
    "forest_reg.fit(housing_finally,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22107.0888400092"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_finally)\n",
    "forest_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51481.61843757 48867.18698326 53592.93924497 54917.35753217\n",
      " 50463.39403515 56776.52132037 51940.08691385 50521.84102811\n",
      " 55729.5024898  53136.5656865 ]\n",
      "Mean 52742.701367175265\n",
      "Standard deviation 2412.0829538615826\n"
     ]
    }
   ],
   "source": [
    "# 随机森林交叉验证\n",
    "forest_scores = cross_val_score(forest_reg,housing_finally,housing_labels,\n",
    "                               scoring = 'neg_mean_squared_error',cv = 10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型调参和网格搜索\n",
    "   1. 手动调整超参数，找到很好的组合很困难\n",
    "   2. 使用GridSearchCV替你进行搜索，提供进行实验的超参数和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
       "                                             max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators='warn', n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = [\n",
    "    {'n_estimators':[3,10,30],'max_features':[2,4,6,8]},\n",
    "    {'bootstrap':[False],'n_estimators':[3,10],'max_features':[2,3,4]},\n",
    "]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state = 42)\n",
    "grid_search = GridSearchCV(forest_reg,param_grid,cv=5,\n",
    "                           scoring = 'neg_mean_squared_error',return_train_score = True)\n",
    "grid_search.fit(housing_finally,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features=6, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=30,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64246.77949402578 {'max_features': 2, 'n_estimators': 3}\n",
      "55869.738265789805 {'max_features': 2, 'n_estimators': 10}\n",
      "53472.049050362606 {'max_features': 2, 'n_estimators': 30}\n",
      "61376.21402981468 {'max_features': 4, 'n_estimators': 3}\n",
      "53846.28747553631 {'max_features': 4, 'n_estimators': 10}\n",
      "51270.16050804186 {'max_features': 4, 'n_estimators': 30}\n",
      "59860.65599776349 {'max_features': 6, 'n_estimators': 3}\n",
      "53114.388232043595 {'max_features': 6, 'n_estimators': 10}\n",
      "50811.40364168747 {'max_features': 6, 'n_estimators': 30}\n",
      "59220.32752543448 {'max_features': 8, 'n_estimators': 3}\n",
      "52884.76688140635 {'max_features': 8, 'n_estimators': 10}\n",
      "50944.352464380354 {'max_features': 8, 'n_estimators': 30}\n",
      "62805.478464758075 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54462.05968306157 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "61117.24602634563 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53022.92700857423 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "60234.49418582252 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52712.90871682337 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score,params in zip(cvres[\"mean_test_score\"],cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score),params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据准备步骤也可以当作超参数来处理，网格搜索会自动查找是否添加不确定的特征，比如是否使用转换器combinedAttre的超参数add_bedroom_per_room\n",
    "# 也可以使用他自动寻找处理问题的最佳方法，比如异常值，缺失特征，特征选择等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                                                   warm_start=False),\n",
       "                   iid='warn', n_iter=10, n_jobs=None,\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002BC74DAA0C8>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002BC74DAAF48>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "    'n_estimators':randint(low = 1,high = 200),\n",
    "    'max_features':randint(low = 1,high = 8),\n",
    "}\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state = 42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg,param_distributions = param_distribs,\n",
    "                               n_iter = 10,cv =5,scoring = 'neg_mean_squared_error',random_state = 42)\n",
    "rnd_search.fit(housing_finally,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49931.83370450304 {'max_features': 7, 'n_estimators': 180}\n",
      "52122.26073885765 {'max_features': 5, 'n_estimators': 15}\n",
      "51463.906813209505 {'max_features': 3, 'n_estimators': 72}\n",
      "51381.385870076425 {'max_features': 5, 'n_estimators': 21}\n",
      "50040.63743808166 {'max_features': 7, 'n_estimators': 122}\n",
      "51436.763292635806 {'max_features': 3, 'n_estimators': 75}\n",
      "51286.345451957626 {'max_features': 3, 'n_estimators': 88}\n",
      "50312.57595748435 {'max_features': 5, 'n_estimators': 100}\n",
      "50995.415279184126 {'max_features': 3, 'n_estimators': 150}\n",
      "65455.38356204712 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score,params in zip(cvres['mean_test_score'],cvres['params']):\n",
    "    print(np.sqrt(-mean_score),params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grid_search' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-95527c57fcf9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'grid_search' is not defined"
     ]
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03])"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.24136048955382883, 'median_income'),\n",
       " (0.16197658459849276, 'income_cat'),\n",
       " (0.10882123891274476, 'INLAND'),\n",
       " (0.10627352591969835, 'population_per_household'),\n",
       " (0.06793261134305181, 'longitude'),\n",
       " (0.06182807241916786, 'latitude'),\n",
       " (0.061404514078416045, 'bedrooms_per_room'),\n",
       " (0.05359825584988402, 'rooms_per_household'),\n",
       " (0.04333950231438806, 'housing_median_age'),\n",
       " (0.019326989179411204, 'population'),\n",
       " (0.01832915558242795, 'total_bedrooms'),\n",
       " (0.01810170268968371, 'total_rooms'),\n",
       " (0.01783695799011688, 'households'),\n",
       " (0.012235325483341324, '<1H OCEAN'),\n",
       " (0.0050080768169210735, 'NEAR OCEAN'),\n",
       " (0.002599382944523225, 'NEAR BAY'),\n",
       " (2.7614323902184926e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attribs = ['rooms_per_household','population_per_household','bedrooms_per_room']\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "attributes\n",
    "sorted(zip(feature_importances,attributes),reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过测试集评估系统\n",
    "   1. 从测试集中获取预测器和标签\n",
    "   2. 运行full_pipline来转换数据\n",
    "   3. 在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49040.18609727207"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = start_test_set.drop('median_house_value',axis = 1)\n",
    "y_test = start_test_set['median_house_value'].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "\n",
    "# df = pd.DataFrame(X_test_prepared)\n",
    "# df.head()\n",
    "# X_test_prepared\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test,final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目启动阶段\n",
    "   1. 展示解决方案\n",
    "   2. 什么有用，什么没有用\n",
    "   3. 基于什么假设\n",
    "   4. 系统有哪些限制\n",
    "   5. 制作漂练的演示文稿，例如收入中位数是预测房价的首要指标\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 启动，监控和维护系统\n",
    "   1. 为生产环境做好准备，将生产数据源接入系统\n",
    "   2. 编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能优化\n",
    "   3. 随着时间推移，模型会渐渐腐坏，定期使用新数据训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估系统性能\n",
    "   1. 需要对系统的预测结果进行抽样评估，需要人工分析，分析师可能是专家，平台工作人员，都需要将人工评估的流水线接入系统\n",
    "   2. 评估输入系统的数据的质量\n",
    "   3. 至少每六个月使用新鲜数据定期训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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